<![CDATA[This research explores the use of machine learning models to predict bike rental demand and optimize targeted marketing campaigns in bike-sharing systems. Utilizing the day.csv and hour.csv datasets, which provide daily and hourly bike rental data, we implemented Decision Tree Regressor, Random Forest Regressor, and Neural Networks (MLPRegressor) to forecast demand. The Random Forest model outperformed the others, achieving an RMSE of 709.08 and an MAE of 469.99 for daily predictions, while the Neural Network demonstrated potential for hourly forecasts. Our analysis revealed significant trends, including increased demand during summer months and peak usage times on weekday mornings and evenings, highlighting the importance of temporal and weather-related factors in predicting bike rental demand. The study's predictive insights allow bike-sharing companies to enhance operational efficiency by optimizing bike allocation during peak periods and reducing idle capacity during off-peak times. Furthermore, the ability to predict demand accurately enables the development of data-driven marketing strategies, such as launching promotions during high-demand periods and targeting specific user groups based on rental patterns. Despite the promising results, challenges such as data preprocessing complexities and computational resource constraints were encountered. Additionally, the study's scope was limited by the available data, suggesting the need for future research to incorporate additional data sources, like real-time traffic conditions and social events, and to explore more advanced machine learning techniques to further improve prediction accuracy. In conclusion, this research underscores the value of predictive analytics in optimizing bike-sharing systems and marketing strategies, contributing to more efficient and user-centric urban mobility solutions.]]>
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